Triple

T12125545
Position Surface form Disambiguated ID Type / Status
Subject Jerry Siegel E288801 entity
Predicate familyName P18 FINISHED
Object Siegel E21266 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Siegel | Statement: [Jerry Siegel, familyName, Siegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siegel
Context triple: [Jerry Siegel, familyName, Siegel]
  • A. Siegel chosen
    Siegel is the surname of Benjamin "Bugsy" Siegel, the infamous American mobster who played a key role in the development of Las Vegas.
  • B. Sigel
    Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
  • C. Schechter
    Schechter is a Jewish surname most notably associated with Solomon Schechter, a prominent rabbi and scholar who helped shape Conservative Judaism.
  • D. Feigel
    Feigel is a surname of Germanic or Yiddish origin, often associated with Central and Eastern European Jewish families.
  • E. Mace Siegel
    Mace Siegel was an American real estate developer and businessman best known as a co-founder and longtime leader of The Macerich Company, one of the largest shopping center owners in the United States.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6ab4b5e4c81909950b17151eb0951 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9157b2a9881908ec0e58cf438fce0 completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f688ad2481909d11782c44b3217f completed May 2, 2026, 1:05 p.m.
Created at: April 8, 2026, 9:49 p.m.